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This tutorial will discuss about the problems that CarbonData address.It shall take you through the identified top use cases of Carbon.+
For big data interactive analysis scenarios, many customers expect sub-second response to query TB-PB level data on general hardware clusters with just a few nodes.+
In the current big data ecosystem, there are few columnar storage formats such as ORC and Parquet that are designed for SQL on Big Data. Apache Hive’s ORC format is
+a columnar storage format with basic indexing capability. However, ORC cannot meet the sub-second query response expectation on TB level data, because ORC format
+performs only stride level dictionary encoding and all analytical operations such as filtering and aggregation is done on the actual data. Apache Parquet is columnar
+storage can improve performance in comparison to ORC, because of more efficient storage organization. Though Parquet can provide query response on TB level data in a
+few seconds, it is still far from the sub-second expectation of interactive analysis users. Cloudera Kudu can effectively solve some query performance issues, but kudu
+is not hadoop native, can’t seamlessly integrate historic HDFS data into new kudu system.
However, CarbonData uses specially engineered optimizations targeted to improve performance of analytical queries which can include filters, aggregation and distinct counts,
+the required data to be stored in an indexed, well organized, read-optimized format, CarbonData’s query performance can achieve sub-second response.
The main motivation behind CarbonData is to provide a single storage format for all the usecases of querying big data on Hadoop. Thus CarbonData is able to cover all use-cases
+into a single storage format.